ArticleAmerican journal of epidemiology2021
Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.
Article in American journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- "We adjusted for race": now what? A systematic review of utilization and reporting of race in American Journal of Epidemiology and Epidemiology, 2020-2021.Epidemiologic reviews · 2023Pooled it
- Pooled it
- Maternal Dietary Factors and the Risk of Autism Spectrum Disorders: A Systematic Review of Existing Evidence.Autism research : official journal of the International Society for Autism Research · 2020Pooled it
- Dates, Polyunsaturated Fatty Acids, Dietary Fiber, and Probiotics as Potential Complementary Modulators of Gut Microbiota and Oxidative Stress: An Integrative Review.Antioxidants (Basel, Switzerland) · 2026Review
- Causal Analysis of Multidimensional Dietary Data to Assess Effects on All-Cause Mortality.Nutrients · 2026Article
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.Nature communications · 2025Review
- Advances in methods for characterizing dietary patterns: A scoping review.medRxiv : the preprint server for health sciences · 2024Article
- Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables.Nutrients · 2023Article
- Interrogating Components of 2 Diet Quality Indices in Pregnancy using a Supervised Statistical Mixtures Approach.The American journal of clinical nutrition · 2023Article
- Editorial: Diet-sleep interaction on cardiometabolic health.Frontiers in public health · 2023Article
- Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography.Journal of clinical medicine · 2022Article
- Joint Associations of Food Groups with All-Cause and Cause-Specific Mortality in the Mr. OS and Ms. OS Study: A Prospective Cohort.Nutrients · 2022Article
- Hidden-mortality risk among patients deemed "low-risk" following high-risk operations.Hepatobiliary surgery and nutrition · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.